84 POOR FOLLOW-UP AND LOW SOCIOECONOMIC STATUS PREDICT CANCER-SPECIFIC MORTALITY IN MEN WITH PROSTATE CANCER

2012 ◽  
Vol 187 (4S) ◽  
Author(s):  
Mohummad Siddiqui ◽  
Alan Paciorek ◽  
Mark S. Litwin ◽  
Aria F. Olumi
Keyword(s):  
Prostate Cancer ◽  
Socioeconomic Status ◽  
Low Socioeconomic Status ◽  
Low Socioeconomic ◽  
Specific Mortality ◽  
Cancer Specific Mortality

scholarly journals Impact of Socioeconomic Status on Return to Work after Acute Ankle Fracture

2020 ◽  
Vol 5 (4) ◽  
pp. 2473011420S0020
Author(s):  
Alessandra L. Falk ◽  
Regina Hanstein ◽  
Chaiyaporn Kulsakdinun
Keyword(s):  
Socioeconomic Status ◽  
Return To Work ◽  
Low Socioeconomic Status ◽  
Ankle Fracture ◽  
Low Ses ◽  
Insurance Status ◽  
Low Socioeconomic ◽  
The Impact ◽  
Zip Code

Category: Ankle; Trauma Introduction/Purpose: Socioeconomic status has been recognized throughout the medical literature, both within orthopedics and beyond, as a factor that influences outcomes after surgery, and can result in substandard care. Within the foot and ankle subspecialty, there is limited data regarding socioeconomic status and post-operative outcomes, with the current literature focusing on outcomes for diabetic feet. However, ankle fractures are among the most common fractures encountered by orthopedic surgeons. While a few studies have explored the impact of ankle fractures on employment and disability status, the effect of socioeconomic status on return to work post operatively has not yet been investigated. The purpose of this study was to determine the impact of low socioeconomic status on return to work. Methods: We retrospectively reviewed 592 medical charts of patients with CPT code 27766, 27792, 27814, 27822, 27823, 27827, 27829, 27826, 27828 from 2015-2018. Included were patients >18 yrs of age who sustained an acute ankle fracture, were employed prior to the injury, and with information on return to work after ankle surgery, zip code, race, ethnicity and insurance status. Excluded were patients who were not employed prior to their injury. Socioeconomic status was either defined by insurance status - Medicaid/Medicare, commercial, or workman’s compensation -, or by assessing socioeconomic status (SES) using medial household per capita income by zip code as generated and reported by the US National Census Bureau’s 2013-2017 American Community Survey 5-Year Estimates. The national dataset was divided into quartiles with the lowest quartile defined as low SES. Patients who had income that fell within this income category were classified as low SES. Results: 174 patients were included with an average follow-up of 10.2months. 22/174 (12.6%) patients didn’t return to work post-operatively. Univariate analysis identified non-sedentary work to decrease the likelihood of return to work (HR:0.637; p=0.03). Patients with a low SES were more prevalent in the no return group compared to the return to work group (86% vs 60%; p=0.028). 95% of patients with low SES were a minority compared to 56% with average/high SES (p<0.005). Patients with low SES had a higher BMI (p=0.026), a longer hospitalization (p=0.04) and more wound complications (p=0.032). Insurance type didn’t affect return to work (p=0.158). Patients with workman’s compensation had a longer follow-up time and a longer time to return to work compared to other insurances (p<0.005 for each comparison). Conclusion: Low socioeconomic status based on income, not insurance type, affected return to work after an ankle fracture ORIF. Patients with workman’s compensation took a longer time to return to work compared to other insurance types. These findings warrants the need to consider socioeconomic status when allocating resources to treat these patients.

The association of very low PSA with increased cancer-specific death in men with high-grade prostate cancer.

2015 ◽  
Vol 33 (7_suppl) ◽  
pp. 62-62
Author(s):  
Brandon Arvin Virgil Mahal ◽  
Ayal Aaron Aizer ◽  
Jason Alexander Efstathiou ◽  
Paul Linh Nguyen
Keyword(s):  
Prostate Cancer ◽  
Reference Group ◽  
High Grade ◽  
Specific Mortality ◽  
National Cohort ◽  
Poorly Differentiated ◽  
Cancer Specific Mortality ◽  
Prostate Cancers ◽  
Very High

62 Background: It has been hypothesized that very low PSAs in men with high-grade prostate cancer could reflect dedifferentiation and a poorer prognosis, but clinical evidence to support this is limited. We sought to determine whether a very low-presenting PSA was associated with greater prostate cancer-specific mortality (PCSM) among men with Gleason score (GS) 8-10 disease. Methods: The Surveillance, Epidemiology and End Results Program was used to identify a national cohort of 328,904 men diagnosed with cT1-4N0M0 prostate cancer between 2004 and 2010. Multivariable Fine-Gray competing-risks regression analysis was used to determine PCSM as a function of PSA level (<2.5 ng/mL, 2.6-4 ng/mL, 4.1-10 ng/mL, 10.1-20 ng/mL, 20.1-40 ng/mL, or >40ng/mL) and GS (8-10 vs. <=7). Results: Median follow-up was 38 months. Among men with GS 8-10 disease, using PSA 4.1-10 as the reference group, the Adjusted HR (AHR) for PCSM for men with PSA level <2.5 was 1.86 (95% CI 1.51-2.29; P<0.001), PSA 2.6-4 was1.44 (1.17-1.78; P<0.001), PSA 10.1-20 was 1.58 (1.39-1.78; P<0.001), PSA 20.1-40 was 2.04 (1.78-2.33; P<0.001), and PSA>40 was 3.19 (2.83-3.59; P<0.001), suggesting a U-shaped distribution. There was a significant interaction between PSA level and GS (Pinteraction<0.001) such that PSA <2.5 only significantly predicted for poorer PCSM among patients with high grade GS 8-10 disease. Conclusions: Among patients with high grade GS 8-10 disease, patients with PSA <2.5 and 2.6-4 appear to have a higher risk for cancer-specific death compared to patients with a 10.1-20 PSA level, supporting the notion that low PSA in GS 8-10 disease may be a sign of underlying aggressive and extremely poorly differentiated or anaplastic low PSA-producing tumors. Patients with low PSA GS 8-10 disease should be considered for clinical trials studying the use of chemotherapy and other novel agents in very-high risk prostate cancers.

Health Literacy and Shared Decision Making for Prostate Cancer Patients with Low Socioeconomic Status

2001 ◽  
Vol 19 (7) ◽  
pp. 684-691 ◽  
Author(s):  
Simon P. Kim ◽  
Sara J. Knight ◽  
Cecilia Tomori ◽  
Kathleen M. Colella ◽  
Richard A. Schoor ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Socioeconomic Status ◽  
Health Literacy ◽  
Cancer Patients ◽  
Shared Decision Making ◽  
Low Socioeconomic Status ◽  
Shared Decision ◽  
Low Socioeconomic

Follow-Up of Premature Infants Of Low Socioeconomic Status

1989 ◽  
Vol 38 (4) ◽  
pp. 246 ◽  
Author(s):  
LINA ZAHR ◽  
STEVEN PARKER ◽  
JEAN COLE ◽  
CINDY ENGLIER
Keyword(s):  
Socioeconomic Status ◽  
Premature Infants ◽  
Low Socioeconomic Status ◽  
Low Socioeconomic

scholarly journals Factors Associated with Low Socioeconomic Status Predict Poor Postoperative Follow-up after Meningioma Resection

2015 ◽  
Vol 77 (03) ◽  
pp. 226-230 ◽  
Author(s):  
Philip Brinson ◽  
Kyle Weaver ◽  
Reid Thompson ◽  
Lola Chambless ◽  
Arash Nayeri
Keyword(s):  
Socioeconomic Status ◽  
Low Socioeconomic Status ◽  
Low Socioeconomic ◽  
Factors Associated

scholarly journals Predicting prostate cancer specific-mortality with artificial intelligence-based Gleason grading

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Ellery Wulczyn ◽  
Kunal Nagpal ◽  
Matthew Symonds ◽  
Melissa Moran ◽  
Markus Plass ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Risk Groups ◽  
Risk Scores ◽  
Pathology Report ◽  
Gleason Grading ◽  
Specific Mortality ◽  
Cancer Specific Mortality ◽  
Open Question

Abstract Background Gleason grading of prostate cancer is an important prognostic factor, but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial intelligence (A.I.) tools have demonstrated Gleason grading on-par with expert pathologists, it remains an open question whether and to what extent A.I. grading translates to better prognostication. Methods In this study, we developed a system to predict prostate cancer-specific mortality via A.I.-based Gleason grading and subsequently evaluated its ability to risk-stratify patients on an independent retrospective cohort of 2807 prostatectomy cases from a single European center with 5–25 years of follow-up (median: 13, interquartile range 9–17). Results Here, we show that the A.I.’s risk scores produced a C-index of 0.84 (95% CI 0.80–0.87) for prostate cancer-specific mortality. Upon discretizing these risk scores into risk groups analogous to pathologist Grade Groups (GG), the A.I. has a C-index of 0.82 (95% CI 0.78–0.85). On the subset of cases with a GG provided in the original pathology report (n = 1517), the A.I.’s C-indices are 0.87 and 0.85 for continuous and discrete grading, respectively, compared to 0.79 (95% CI 0.71–0.86) for GG obtained from the reports. These represent improvements of 0.08 (95% CI 0.01–0.15) and 0.07 (95% CI 0.00–0.14), respectively. Conclusions Our results suggest that A.I.-based Gleason grading can lead to effective risk stratification, and warrants further evaluation for improving disease management.

scholarly journals MP21-11 IS LOW SOCIOECONOMIC STATUS ASSOCIATED WITH WORSE TUMOR BIOLOGY FOR MEN WITH PROSTATE CANCER?

2018 ◽  
Vol 199 (4S) ◽  
Author(s):  
Adam Weiner ◽  
Mohammed Alshalalfa ◽  
Elai Davicioni ◽  
Nicholas Erho ◽  
Nick Fishbane ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Socioeconomic Status ◽  
Low Socioeconomic Status ◽  
Tumor Biology ◽  
Low Socioeconomic

Associations of rural residence and low socioeconomic status on the risk of new onset cardiovascular disease (CVD) in cancer patients.

2020 ◽  
Vol 38 (29_suppl) ◽  
pp. 90-90
Author(s):  
Atul Batra ◽  
Shiying Kong ◽  
Rodrigo Rigo ◽  
Winson Y. Cheung
Keyword(s):  
Socioeconomic Status ◽  
Educational Attainment ◽  
Cancer Patients ◽  
Low Socioeconomic Status ◽  
Low Income ◽  
Low Ses ◽  
Rural Residence ◽  
Low Socioeconomic ◽  
Low Education

90 Background: Cancer patients are predisposed to CVD due to cancer treatments and shared risk factors (smoking/physical inactivity). We aimed to assess if rural residence and low socioeconomic status (SES) modify the risk of developing CVD. Methods: Patients diagnosed with non-metastatic solid organ cancers without baseline CVD in a large Canadian province from 2004 to 2017 were identified using the population-based registry. Postal codes were linked with Census data to determine rural residence as well as neighborhood-level income and educational attainment. Low income was defined as <46000 CAD/annum; low education was defined as a neighborhood in which <80% attended high school. Myocardial infarction, congestive heart failure, arrythmias and cerebrovascular accident constituted as CVD.We performed logistic regression analyses to examine the associations of rural residence and low SES with the development of CVD, adjusting for measured confounding variables. Results: We identified 81,275 patients diagnosed with cancer without pre-existing CVD. The median age was 62 years and 54.2% were women. The most prevalent cancer types included breast (28.6%), prostate (23.1%), and colorectal (14.9%). At a median follow-up of 68 months, 29.4% were diagnosed with new CVD. The median time from cancer diagnosis to CVD was 29 months. Rural patients (32.3 vs 28.4%,P < .001) and those with low income (30.4% vs 25.9%,P < .001) or low educational attainment (30.7% vs 27.6%,P < .001) experienced higher rates of CVD. After adjusting for baseline factors and treatment, rural residence (odds ratio[OR], 1.07; 95% confidence interval[CI], 1.04-1.11;P < .001), low income (OR,1.17;95%CI,1.12-1.21;P < .001) and low education (OR,1.08;95%CI,1.04-1.11;P < .001) continued to associate with higher odds of CVD. Further, patients with colorectal cancer were more likely to develop CVD compared with other tumors (OR,1.12;95% CI,1.04-1.16;P = .001). A multivariate Cox regression model showed that patients with low SES were more likely to die, but patients residing rurally were not. Conclusions: Approximately one-third of cancer survivors develop CVD on follow-up. Despite universal healthcare, marginalized populations experience different CVD risk profiles that should be considered when operationalizing lifestyle modification strategies and cardiac surveillance programs. [Table: see text]

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